minimum¶

paddle. minimum ( x, y, name=None ) [source]

Compare two tensors and return a new tensor containing the element-wise minima. The equation is:

\[out = min(x, y)\]

Note

`paddle.minimum` supports broadcasting. If you want know more about broadcasting, please refer to Introduction to Tensor .

Parameters
• x (Tensor) – the input tensor, it’s data type should be float32, float64, int32, int64.

• y (Tensor) – the input tensor, it’s data type should be float32, float64, int32, int64.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor. If x, y have different shapes and are “broadcastable”, the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y.

Examples

```>>> import paddle

>>> x = paddle.to_tensor([[1, 2], [7, 8]])
>>> y = paddle.to_tensor([[3, 4], [5, 6]])
>>> res = paddle.minimum(x, y)
>>> print(res)
Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1, 2],
[5, 6]])

>>> x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]])
>>> y = paddle.to_tensor([3, 0, 4])
>>> res = paddle.minimum(x, y)
>>> print(res)
Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[[1, 0, 3],
[1, 0, 3]]])

>>> x = paddle.to_tensor([2, 3, 5], dtype='float32')
>>> y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32')
>>> res = paddle.minimum(x, y)
>>> print(res)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[1. , nan, nan])

>>> x = paddle.to_tensor([5, 3, float("inf")], dtype='float64')
>>> y = paddle.to_tensor([1, -float("inf"), 5], dtype='float64')
>>> res = paddle.minimum(x, y)
>>> print(res)
Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True,
[ 1.  , -inf.,  5.  ])
```